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A Study of the Computational Space of Facial Expressions of Emotion

面部表情情感的计算空间研究

基本信息

批准号：
8266468
负责人：
Aleix M Martinez
金额：
$ 36.6万
依托单位：
OHIO STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-30 至 2015-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8266468
关键词：
Acute Address Affect Aging-Related Process Anger Arts Autistic Disorder Behavior Child Abuse Classification Code Cognition Cognitive Complex Computer Simulation Computer Vision Systems Conscious Cues Depressed mood Dimensions Duchenne muscular dystrophy Emotions Evolution Eye diseases Face Face Processing Facial Expression Facial Muscles Fright Goals Happiness Human Huntington Disease Image Individual Lead Movement Muscle Neurons Oral cavity Perception Play Positioning Attribute Primates Process Protocols documentation Research Resolution Role Schizophrenia Shapes Social Interaction System Time To specify Visual Visual impairment Visual system structure base cognitive system computer human interaction computer studies court design human subject millisecond psychologic research study showing emotion visual process visual processing

项目摘要

Project Summary Past research has been very successful in defining how facial expressions of emotion are produced, including which muscle movements create the most commonly seen expressions. These facial expressions of emotion are then interpreted by our visual system. Yet, little is known about how these facial expressions are recognized. The overarching goal of this proposal is to define the form and dimensions of the cognitive (computational) space used in this visual recognition. In particular, this proposal will study the following three hypotheses: Although facial expressions are produced by a complex set of muscle movements, expressions are generally easily identified at different spatial and time resolutions. However, it is not know what these limits are. Our first hypothesis (H1) is that recognition of facial expressions of emotion can be achieved at low resolutions and after short exposure times. In Aim 1, we define experiments to determine how many pixels and milliseconds (ms) are needed to successfully identify different emotions. The fact that expressions of emotion can be recognized quickly at low resolution indicates that simple features robust to image manipulation are employed. Our second hypothesis (H2) is that the recognition of facial expressions of emotion is partially accomplished by an analysis of configural features. Configural cues are known to play an important role in other face recognition tasks, but their role in the processing of expressions of emotion is not yet well understood. Aim 2 will identify a number of these configural cues. We will use real images of faces, manipulated versions of these face images, and schematic drawings. It is also known that shape features play a role in facial expressions (e.g., the curvature of the mouth in happiness). In Aim 3, we define a shape-based computational model. Our hypothesis (H3) is that the configural and shape features are defined as deviations from a mean (or norm) face as opposed to being described as a set of independent exemplars (Gnostic neurons). The importance of this computational space is not only to further justify the results of the previous aims, but to make new predictions that can be verified with additional experiments with human subjects.

项目总结